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Found 9 papers, 2 papers with code
Operator SVD with Neural Networks via Nested Low-Rank Approximation
Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scientific computing problems.
A Geometric Framework for Neural Feature Learning
We present a novel framework for learning system design based on neural feature extractors.
Kernel Subspace and Feature Extraction
We study kernel methods in machine learning from the perspective of feature subspace.
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning
Current transfer learning algorithm designs mainly focus on the similarities between source and target tasks, while the impacts of the sample sizes of these tasks are often not sufficiently addressed.
On Distributed Learning with Constant Communication Bits
Specifically, we consider the distributed hypothesis testing (DHT) problem where two distributed nodes are constrained to transmit a constant number of bits to a central decoder.
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
Moreover, we develop a generalized form of the softmax function to effectively implement maximum likelihood estimation in an end-to-end manner.
An Information-theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data
In this paper, we propose an information-theoretic approach to design the functional representations to extract the hidden common structure shared by a set of random variables.
An Information Theoretic Interpretation to Deep Neural Networks
It is commonly believed that the hidden layers of deep neural networks (DNNs) attempt to extract informative features for learning tasks.
An Efficient Approach to Informative Feature Extraction from Multimodal Data
We further generalize the framework to handle more than two modalities and missing modalities.
